Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort

PLoS Comput Biol. 2019 Jun 25;15(6):e1007157. doi: 10.1371/journal.pcbi.1007157. eCollection 2019 Jun.


Estimation of liver function is important to monitor progression of chronic liver disease (CLD). A promising method is magnetic resonance imaging (MRI) combined with gadoxetate, a liver-specific contrast agent. For this method, we have previously developed a model for an average healthy human. Herein, we extended this model, by combining it with a patient-specific non-linear mixed-effects modeling framework. We validated the model by recruiting 100 patients with CLD of varying severity and etiologies. The model explained all MRI data and adequately predicted both timepoints saved for validation and gadoxetate concentrations in both plasma and biopsies. The validated model provides a new and deeper look into how the mechanisms of liver function vary across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate. These mechanisms are shared across many liver functions and can now be estimated from standard clinical images.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cohort Studies
  • Female
  • Gadolinium DTPA / pharmacokinetics
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Liver / diagnostic imaging*
  • Liver / metabolism*
  • Liver Cirrhosis / diagnostic imaging
  • Liver Cirrhosis / metabolism
  • Liver Function Tests
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Models, Biological
  • Reproducibility of Results
  • Young Adult


  • gadolinium ethoxybenzyl DTPA
  • Gadolinium DTPA

Grant support

Financial support from the Swedish Research Council (; VR/MH, #2007-2884 as well as VR/NT #2014-6157 both to PL), the Medical Research council of Southeast Sweden (; FORSS #12621 to PL), Vinnova (; #2013-01314 to PL), Linköping University, CENIIT (; #15.09 to GC), the Swedish fund for research without animal experiments (; #Nytänk2015 to GC) are gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.